Colors in R STAT 133 Gaston Sanchez Department of Statistics, - - PowerPoint PPT Presentation

Colors in R

STAT 133 Gaston Sanchez

Department of Statistics, UC–Berkeley gastonsanchez.com github.com/gastonstat/stat133 Course web: gastonsanchez.com/stat133

Colors in R

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Colors in plots

Colors on objects

In R plots, many objects can take on different colors

◮ points ◮ lines ◮ axes (and tick marks) ◮ filling areas ◮ borders ◮ text ◮ legends ◮ background 3

Colored points

x <- -4:4 y <- x^2 plot(x, y, pch = 19, cex = 3, col = rainbow(length(x)))

• −4

−2 2 4 5 10 15 x y

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Colored lines

# data Time <- 0:120 Period1 <- cos(2 * pi * Time/120) Period2 <- cos(2 * pi * Time/90) Period3 <- cos(2 * pi * Time/150) Periods <- data.frame( Period1 = Period1, Period2 = Period2, Period3 = Period3) # graphical parameters line_cols <- c("#5984d4", "#d45984", "#84d459") line_types <- c("solid", "dotted", "dashed") # plot matplot(Periods, type = "l", xlab = "Time", ylab = "Expression" , col = line_cols, lty = line_types, lwd = 3) legend("bottomleft", c("120 min period", " 90 min period","150 min period"), col = line_cols, lty = line_types) 5

Colored lines

20 40 60 80 100 120 −1.0 −0.5 0.0 0.5 1.0 Time Expression 120 min period 90 min period 150 min period

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Colors can dramatically impact how we perceive a graphic and what we see in the data.

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Why Colors?

Importance of Color

◮ Color isn’t just about making your charts look pretty ◮ Color can serve as a visual cue just like the height of a bar

• r the position of a dot

◮ R provides a straighforward way to modify colors 8

Naming Colors

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Naming colors

Specifying colors

There are various ways to specify colors in R

◮ by using the color’s name (in English): e.g. "turquoise" ◮ by using a hexadecimal string: "#FFAA00" ◮ by using standard color space functions: e.g. rgb() 10

Function colors()

The easiest way to specify a color in R is simply to use the color’s name. The R function colors() provides the names of 657 available colors

# first 30 colors colors()[1:30] ## [1] "white" "aliceblue" "antiquewhite" "antiquewhite1" ## [5] "antiquewhite2" "antiquewhite3" "antiquewhite4" "aquamarine" ## [9] "aquamarine1" "aquamarine2" "aquamarine3" "aquamarine4" ## [13] "azure" "azure1" "azure2" "azure3" ## [17] "azure4" "beige" "bisque" "bisque1" ## [21] "bisque2" "bisque3" "bisque4" "black" ## [25] "blanchedalmond" "blue" "blue1" "blue2" ## [29] "blue3" "blue4" 11

Function colors()

# first 10 colors() pie(rep(1, 10), col = colors()[1:10], labels = colors()[1:10])

white aliceblue antiquewhite antiquewhite1 antiquewhite2 antiquewhite3 antiquewhite4 aquamarine aquamarine1 aquamar

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More colors()

Use grep() to get colors of a given name

# orangey colors colors()[grep("orange", colors())] ## [1] "darkorange" "darkorange1" "darkorange2" "darkorange3" "darkorange4" ## [6] "orange" "orange1" "orange2" "orange3" "orange4" ## [11] "orangered" "orangered1" "orangered2" "orangered3" "orangered4" 13

Orangey colors()

# orangey colors()

• ranges <- colors()[grep("orange", colors())]

pie(rep(1, length(oranges)), col = oranges, labels = oranges)

darkorange darkorange1 darkorange2 darkorange3 darkorange4

• range
• range1
• range2
• range3
• range4
• rangered
• rangered1
• rangered2
• rangered3
• rangered4

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657 R built−in colors

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400 401 402 403 404 405 406 407 408 409 410 411 412 413 414 415 416 417 418 419 420 421 422 423 424 425 426 427 428 429 430 431 432 433 434 435 436 437 438 439 440 441 442 443 444 445 446 447 448 449 450 451 452 453 454 455 456 457 458 459 460 461 462 463 464 465 466 467 468 469 470 471 472 473 474 475 476 477 478 479 480 481 482 483 484 485 486 487 488 489 490 491 492 493 494 495 496 497 498 499 500 501 502 503 504 505 506 507 508 509 510 511 512 513 514 515 516 517 518 519 520 521 522 523 524 525 526 527 528 529 530 531 532 533 534 535 536 537 538 539 540 541 542 543 544 545 546 547 548 549 550 551 552 553 554 555 556 557 558 559 560 561 562 563 564 565 566 567 568 569 570 571 572 573 574 575 576 577 578 579 580 581 582 583 584 585 586 587 588 589 590 591 592 593 594 595 596 597 598 599 600 601 602 603 604 605 606 607 608 609 610 611 612 613 614 615 616 617 618 619 620 621 622 623 624 625 626 627 628 629 630 631 632 633 634 635 636 637 638 639 640 641 642 643 644 645 646 647 648 649 650 651 652 653 654 655 656 657

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# Code by Earl F. Glynn SetTextContrastColor <- function(color) { ifelse( mean(col2rgb(color)) > 127, "black", "white") } TextContrastColor <- unlist(lapply(colors(), SetTextContrastColor)) colCount <- 25 # number per row rowCount <- 27

• p <- par(mar = c(0, 0, 4, 0))

plot(c(1, colCount), c(0, rowCount), type = "n", axes=FALSE, ylab = "", xlab = "", ylim = c(rowCount, 0)) title("657 R built-in colors") for (j in 0:(rowCount-1)) { base <- j * colCount remaining <- length(colors()) - base RowSize <- ifelse(remaining < colCount, remaining, colCount) rect((1:RowSize)-0.5, j-0.5, (1:RowSize)+0.5, j+0.5, border = "black", col = colors()[base + (1:RowSize)]) text((1:RowSize), j, paste(base + (1:RowSize)), cex = 0.7, col = TextContrastColor[base + (1:RowSize)]) } par(op)

http://research.stowers-institute.org/efg/R/Color/Chart/index.htm 16

Gray colors()

Note that there is a wide range of gray (grey) colors:

# gray and grey colors grays <- colors()[grep("gr[a|e]y", colors())] length(grays) ## [1] 224 head(grays, 10) ## [1] "darkgray" "darkgrey" "darkslategray" "darkslategray1" ## [5] "darkslategray2" "darkslategray3" "darkslategray4" "darkslategrey" ## [9] "dimgray" "dimgrey" 17

Example

# color argument 'col' plot(mtcars$mpg, mtcars$hp, pch = 19, col = "blue", cex = 1.2)

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15 20 25 30 50 150 250 mtcars$mpg mtcars$hp

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RGB Color Model

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RGB Colors

About RGB color

◮ Computers create the colors we see on a monitor by

combining 3 primary colors of light:

– red – green – blue

◮ This combination is known as RGB color model ◮ Each color light is also referred to as a channel 20

Red-Green-Blue

A computer screen displays a color by combining red light, green light and blue light, the so-called RGB model.

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RGB Colors

Values of RGB colors

◮ Any color you see on a monitor can be described by a

series of 3 numbers (in the following order):

– a red value – a green value – a blue value

◮ e.g. red=30, green=200, blue=180 22

RGB Colors

Values of RGB colors

◮ The amount of light in each color channel is typically

described on a scale from 0 (none) to 255 (full-blast)

◮ Alternatively, scales can be provided as percent values from

0 (none) to 1 (100%)

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RGB Colors

Some reference colors: RGB Values Color (255, 0, 0) red (0, 255, 0) green (0, 0, 255) blue (0, 0, 0) black (255, 255, 255) white The closer the three values get to 255 (100%), the closer the resulting color gets to white

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rgb() colors

R provides the function rgb() to specify RGB colors

# 0 to 1 (default scale) rgb(red = 1, green = 0, blue = 0) ## [1] "#FF0000" # 0 to 255 rgb(red = 255, green = 0, blue = 0, maxColorValue = 255) ## [1] "#FF0000"

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Example

# color argument 'col' plot(mtcars$mpg, mtcars$hp, pch = 19, cex = 1.2, col = rgb(255, 0, 0, max = 255))

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15 20 25 30 50 150 250 mtcars$mpg mtcars$hp

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Colors in Hexadecimal Notation

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Hex Colors

Storing RGB colors in decimal notation would require 9 digits:

255|255|255

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Hex Colors

Storing RGB colors in decimal notation would require 9 digits:

255|255|255

In order to have a more efficient storage system, computers use hexadecimal digits

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Hex Colors

Colors in hexadecimal notation

A color can also be specified as a string beginning with a hash symbol "#" and followed by six hexadecimal digits

◮ "#FF0000" (red) ◮ "#00FF00" (green) ◮ "#0000FF" (blue) ◮ "#FF6347" (tomato)

This is actually the output format of rgb()

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Example

# color argument 'col' plot(mtcars$mpg, mtcars$hp, pch = 19, cex = 1.2, col = "#559944")

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15 20 25 30 50 150 250 mtcars$mpg mtcars$hp

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Hexadecimal code

Hexadecimal: numeral system with base 16, or hex

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16

1 2 3 4 5 6 7 8 9 A B C D E F

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Hexadecimal code

R uses the hexadecimal system to name colors

◮ The hexadecimal representation uses 16 different symbols ◮ The 16 symbols are all 10 digits (0-9) and 6 first letters

(A-F)

◮ The digits 0-9 represent values zero to nine ◮ The letters A, B, C, D, E, F (or a, b, c, d, e, f) represent

values ten to fifteen

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Hexadecimal Notation

Decimal and Hexadecimal

## 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 ## decimal 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 ## hexadecimal 0 1 2 3 4 5 6 7 8 9 A B C D E F

Two hexadecimal digits together can make 16 × 16 = 256 different values (from 0 to 255) Six hexadecimal digits together can make 166 = 16, 777, 216 different values.

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Hexadecimal code

Hexadecimal color: #975015

◮ # declares that this “is a hex number” ◮ The other six are really three sets of pairs ◮ Each pair controls one primary additive color ◮ The first pair corresponds to red ◮ The second pair corresponds to green ◮ The last pair corresponds to blue 34

# _ _ _ _ _ _

red green blue

0 1 2 3 4 5 6 7 8 9 A B C D E F

There are 256 possible shades each of red, green, and blue

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RGB Hexadecimal Notation

1 2 3 4 5 6 7 8 9 A B C D E F d0 30 10 c0 d0 50

#d03010 #c0d050

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Hexadecimal code

Hexadecimal digits

◮ 0 is the smallest representation of a color (absence of color) ◮ F is 15 times the intensity of color 0 ◮ 00 is equal to zero hue ◮ FF is equal to a pure color ◮ #000000 black ◮ #FFFFFF white ◮ equal digits produce a shade of gray 37

RGB and Hex

# 85 A0 F2

8x16 + 5x1 = 133 10x16 + 0x1 = 160 15x16 + 2x1 = 242 rgb(133, 160, 242, maxColorValue = 255)

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RGB Cube Representation

http://drmoron.org/is-black-a-color/ 39

Your turn

Orange in hex-code is:

A) #FFA500 B) #3EDCD9 C) #A500FF D) #E88472

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Your turn

What color is RGB: 89, 132, 212

A) Orange B) Blue C) Black D) Yellow

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RGB Inconvenience

RGB drawbacks

◮ The RGB color model is the most commonly used ◮ However, specifying RGB colors in not intuitive ◮ It is no straightforward how to make a color stronger,

darker or lighter with RGB values

◮ It is also hard to “read” RGB values (and being able to

identify the corresponding hue)

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Cylindrical-coordinate Representations

Cylindrical Models

◮ An alternative to RGB values is the HSV model ◮ HSV: Hue (color), Saturation, Value ◮ HSV rearranges the geometry of RGB following cyclindrical

coordinates

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HSV representation

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Cylindrical-coordinate Representations

HSV Models

◮ Hue values are measured in degrees around the circle

– Red at 0 degrees – Green at 120◦ – Blue at 240◦ – other colors in between

◮ Saturation is a percentage value from 0 (gray) to 1 (full

blast)

◮ Value is also a percentage value from 0 (darkest) to 1

(lightest)

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Function hsv()

R provides the function hsv() which takes an HSV triplet

hsv(h = 1, s = 0.8, v = 0.9)

Note that the Hue value ranges from 0 (0 degrees) to 1 (360 degrees)

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Example

# hsv color plot(mtcars$mpg, mtcars$hp, pch = 19, cex = 1.2, col = hsv(h = 1, s = 1, v = 1))

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15 20 25 30 50 150 250 mtcars$mpg mtcars$hp

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HSV

About HSV

◮ HSV is a more intuitive system ◮ HSV is also more perceptually relevant ◮ Once you select a hue, it is easy to make it stronger,

darker, or lighter

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Other Representations

HCL Model

◮ Another model is HCL ◮ HCL: Hue, Chroma, Luminance ◮ Hue values are measured in degrees around the circle

– Red at 0 degrees – Green at 120◦ – Blue at 240◦ – other colors in between

◮ Luminance is also a percentage value from 0 (darkest) to

100 (lightest)

◮ Chroma depends on Hue and Luminance 49

Example

# hcl color plot(mtcars$mpg, mtcars$hp, pch = 19, cex = 1.2, col = hcl(h = 3550, c = 75, l = 50))

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15 20 25 30 50 150 250 mtcars$mpg mtcars$hp

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Color Space

HSV Colors by space model

R provides the functions hsv() and hcl()

◮ HSV (Hue, Saturation, Value) triplet:

hsv(h = 1, s = 0.8, v = 0.9)

◮ HCL (Hue, Chroma, Luminance) triplet:

hcl(h = 1, c = 35, l = 85)

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Color Space Models

Color Model

A color model is an abstract mathematical model describing the way colors can be represented as tuples of numbers, typically as three or four values or color components.

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About colors

RGB Model

The RGB model uses an additive color mixing with primary colors of red, green, and blue, each of which stimulates one of the three types of the eye’s color receptors.

RGB usefulness

Mixtures of light of these primary colors cover a large part of the human color space and thus produce a large part of human color experiences.

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Semitransparent Colors

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Semitransparent Colors

Color transparency

All R colors are stored with an alpha transparency channel.

◮ An alpha value of 0 means fully transparent ◮ An alpha value of 1 means fully opaque 55

Semitransparent Colors

Alpha values

When using any of the color space functions, transparency is indicated with the parameter alpha:

◮ rgb(1, 0, 0, alpha = 0.5) ◮ hsv(h = 1, s = 0.8, v = 0.8, alpha = 0.5) ◮ hcl(h = 0, c = 35, l = 85, alpha = 0.5) 56

Semitransparent Colors

Hex color transparency

When using hexadecimal notation, transparency is indicated by using a hexadecimal string of eight digits. The last two digits indicate transparency:

◮ a hex digit "00" indicates an alpha value of 0 (fully

transparent)

◮ a hex digit "FF" indicates an alpha value of 1 (fully

• paque)

For example, "#FFA50080" specifies a semitransparent orange. Note that "#FFA500" is equivalent to "#FFA500FF"

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Hex notation with alpha channel

red green blue

0 1 2 3 4 5 6 7 8 9 A B C D E F

alpha

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Transparency

Use transparency for overlapping data

◮ The number of lines or points on a graph can obscure

• ther values that lie behind them.

◮ One option to present many overlapping values is to make

the color values partially transparent

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Example

# transparent color plot(iris$Petal.Length, iris$Sepal.Length, pch = 19, cex = 1.2, col = "#33994088")

1 2 3 4 5 6 7 4.5 5.5 6.5 7.5 iris$Petal.Length iris$Sepal.Length

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Converting Colors

Converting colors functions

R provides a set of functions for converting between different color spaces function package col2rgb() "grDevices" hex2RGB() "colorspace" convertColor() "grDevices"

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# turquoise into RGB col2rgb("turquoise") ## [,1] ## red 64 ## green 224 ## blue 208 # turquoise (hex) into RGB library("colorspace") hex2RGB("#40E0D0") ## R G B ## [1,] 0.2509804 0.8784314 0.8156863 # color 'red' sRGB into Luv convertColor(t(col2rgb("red")/255), from = "sRGB", to = "Luv") ## L u v ## [1,] 53.48418 175.3647 37.80017 62

Color Palettes

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Color Sets

Converting colors functions

Usually more than one color is required within a single plot, and in such cases we need to select colors that are aesthetically pleasing or related in some way. For this, R provides several ways to specifcy or create color schemes and palettes.

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R Default Palettes

Basic Color Palettes

R provides a set of functions to generate basic color sets.

◮ rainbow() ◮ heat.colors() ◮ topo.colors() ◮ terrain.colors() ◮ cm.colors() ◮ gray.colors() 65

Function rainbow()

# 8 colors rainbow n <- 8 pie(rep(1, n), col = rainbow(n))

1 2 3 4 5 6 7 8

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Function heat.colors()

# heat colors (8 values) n <- 8 pie(rep(1, n), col = heat.colors(n))

1 2 3 4 5 6 7 8

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Function terrain.colors()

# terrain colors (8 values) n <- 8 pie(rep(1, n), col = terrain.colors(n))

1 2 3 4 5 6 7 8

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Function topo.colors()

# topo colors (8 values) n <- 8 pie(rep(1, n), col = topo.colors(n))

1 2 3 4 5 6 7 8

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Function cm.colors()

# cyan-magenta colors (8 values) n <- 8 pie(rep(1, n), col = cm.colors(n))

1 2 3 4 5 6 7 8

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Function gray.colors()

# gray colors (8 values) n <- 8 pie(rep(1, n), col = gray.colors(n))

1 2 3 4 5 6 7 8

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Color Brewer

The R package "RColorBrewer" (by Erich Neuwirth) provides nice color schemes designed by Cynthia Brewer and described at http://colorbrewer2.org

# remember to install RColorBrewer first! library(RColorBrewer) # display available schemes display.brewer.all()

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R Color Brewer Schemes

BrBG PiYG PRGn PuOr RdBu RdGy RdYlBu RdYlGn Spectral Accent Dark2 Paired Pastel1 Pastel2 Set1 Set2 Set3 Blues BuGn BuPu GnBu Greens Greys Oranges OrRd PuBu PuBuGn PuRd Purples RdPu Reds YlGn YlGnBu YlOrBr YlOrRd

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Brewer palette

The main function of "RColorBrewer" is brewer.pal() that allows you to select a color palette by specifying the name and size of the palette.

# palette "BuPu" (blue-purple) n <- 7 pie(rep(1, n), col = brewer.pal(n, "BuPu"))

1 2 3 4 5 6 7

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Brewer palette

# palette "Spectral" n <- 11 pie(rep(1, n), col = brewer.pal(n, "Spectral"))

1 2 3 4 5 6 7 8 9 10 11

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More about color spaces

◮ The package "colorspace" provides functions to create

colors in a variety of color spaces, plus functions to convert between color spaces.

◮ Another useful package is "munsell", which is designed

• n the Munsell color space.

◮ There is also the "dichromat" package that provides a

series of palettes that are suitable for people with color blindness deficiencies.

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Color Wheel

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Color Wheel

Understanding the Color Wheel

The color wheel helps you visualize the relationships that colors have to one another. The wheel shows different colors evenly apart.

#FF6347 #FFBF47 #E3FF47 #87FF47 #47FF63 #47FFBF #47E3FF #4787FF #6347FF #BF47FF #FF47E3 #FF4787

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Color Wheel

The R package "colortools" provides the function wheel(). The function takes the name of a color, and produces a corresponding color wheel with the specified number of slices.

# remember to install colortools first! library(colortools) # color wheel for 'tomato' wheel("#FFB973", bg = "white")

#FFB973 #FFFF73 #B9FF73 #73FF73 #73FFB9 #73FFFF #73B9FF #7373FF #B973FF #FF73FF #FF73B9 #FF7373

## [1] "#FFB973" "#FFFF73" "#B9FF73" "#73FF73" "#73FFB9" "#73FFFF" "#73B9FF" ## [8] "#7373FF" "#B973FF" "#FF73FF" "#FF73B9" "#FF7373"

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# Adjacent (analogous) adjacent("tomato", title = FALSE)

#FF6347 #FFBF47 #E3FF470D #87FF470D #47FF630D #47FFBF0D #47E3FF0D #4787FF0D #6347FF0D #BF47FF0D #FF47E30D #FF4787

# Complementary complementary("tomato", title = FALSE)

#FF6347 #FFBF470D #E3FF470D #87FF470D #47FF630D #47FFBF0D #47E3FF #4787FF0D #6347FF0D #BF47FF0D #FF47E30D #FF47870D

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# split complementary splitComp("tomato", title = FALSE)

#FF6347 #FFBF470D #E3FF470D #87FF470D #47FF630D #47FFBF #47E3FF0D #4787FF #6347FF0D #BF47FF0D #FF47E30D #FF47870D

# triadic triadic("tomato", title = FALSE)

#FF6347 #FFBF470D #E3FF470D #87FF470D #47FF63 #47FFBF0D #47E3FF0D #4787FF0D #6347FF #BF47FF0D #FF47E30D #FF47870D

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# tetradic tetradic("tomato", title = FALSE)

#FF6347 #FFBF470D #E3FF47 #87FF470D #47FF630D #47FFBF0D #47E3FF #4787FF0D #6347FF #BF47FF0D #FF47E30D #FF47870D

# square square("tomato", title = FALSE)

#FF6347 #FFBF470D #E3FF470D #87FF47 #47FF630D #47FFBF0D #47E3FF #4787FF0D #6347FF0D #BF47FF #FF47E30D #FF47870D

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Ramp Palettes

colorRampPalette()

R also offers the functions colorRamp() and colorRampPalette(). They are not color set generators, but color set function generators.

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Ramp Palettes

colorRamp()

colorRamp() produces a function for creating colors based on a sequence of values in the range 0 to 1. The output is a numeric matrix of RGB color values

red_green <- colorRamp(c("red", "green")) red_green( (0:4)/4 ) ## [,1] [,2] [,3] ## [1,] 255.00 0.00 ## [2,] 191.25 63.75 ## [3,] 127.50 127.50 ## [4,] 63.75 191.25 ## [5,] 0.00 255.00

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Ramp Palettes

colorRampPalette()

colorRampPalette() produces a function that generates n colors

• range_blue <- colorRampPalette(c("orange", "blue"))
• range_blue(5)

## [1] "#FFA500" "#BF7B3F" "#7F527F" "#3F29BF" "#0000FF"

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Colors and Graphic Devices

Color appearance

The final appearance of a color can vary considerably depending

• n whether it appears on a screen, printed on paper, or

displayed through a projector.

Device Dependency of Color

The colors that R sends to a graphics device are sRGB colors; the reason is that most computer monitors are set up to work with sRGB model.

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Some Resources

◮ Color-Hex

http://www.color-hex.com/

◮ Paletton

http://paletton.com

◮ Adobe color scheme

https://color.adobe.com/create/color-wheel/

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